Field
Various signal processing systems may benefit from appropriate control based on selected parameters. For example, feedforward-based signal or noise cancellation systems may benefit from adaptive frequency band weighting within time domain.
Description of the Related Art
Digitally controlled adaptive feedforward-based signal or noise cancellation systems are widely used within many application areas to optimize system performance. Many times the feedforward noise or signal cancellation algorithm can use time-domain signal waveforms to control the adaptation process. Within telecommunication systems, for example, the time domain signals tend to be broadband and the signal or noise cancellation can be much more difficult as compared to such cancellation in narrow band systems.
Fifth generation (5G) and subsequent technology may increasingly rely on broadband systems. Also, carrier aggregation within current radio systems broadens the signal bandwidth. The widening of signal bandwidth may render existing adaptive algorithms and feedforward systems ineffective.
FIG. 1 illustrates example application areas for feed-forward based signal cancellation use. FIG. 1, in particular, illustrates a self-interference cancellation half-duplex architecture at (a) and a self-interference cancellation, full-duplex architecture at (b). Amplitude and phase shifters or I&Q vector modulators may be sufficient if the required signal cancellation bandwidth is narrow. However, when the cancellation bandwidth is broad, the non-idealities may shape the unwanted signal delay and frequency response over broadband in such manner that it is difficult to cancel it with an analog domain signal canceller. Therefore, more advanced analog signal cancellation circuits may be needed.
Also, the adaptive time-domain algorithms performance starts to be limited for broadband signals. Such limitations may particularly acute if a complex AFIR or AIIR analog canceller needs to be adaptively controlled.
Time-domain adaptive algorithms tend to optimize the performance into frequencies that have the highest probability to occur. As an example, a simple LMS algorithm signal cancellation simulation is used to show that tendency.
FIG. 2 illustrates an example feedforward signal cancellation architecture. As shown in FIG. 2, there can be an input signal vi which goes through an unknown system channel. The signal vi can be copied and taken as input to adaptive control of complex gain element FIR. The other control signal of the adaptive control can be the feedback signal formed by the subtraction output of channel and FIR. Hence, if the signal canceller FIR can produce equal amplitude and delay but opposite phase output signal to the channel output signal, there will be zero at the main output, thus ve would be zero, where ve is an error signal. In this example vi can be an extremely broadband OFDM signal. The channel can have a slight frequency response deviation.
FIG. 3 illustrates frequency location of best cancellation over broadband over time versus signal instantaneous frequency. Thus, FIG. 3 shows the calculated instantaneous frequency of vi and the frequency location of best cancellation over time. As can be seen from FIG. 3, the best cancellation follows the instantaneous frequency. This is because the input signal vi and error signal ve are used as adaptation control signals. Therefore, the cancellation performance follows the signal instantaneous frequency because that is controlling the way in which the cancellation is performed.
The corresponding spectrum over long time is shown in FIG. 4. More particularly, FIG. 4 illustrates spectrum of signal cancellation broad band performance at one time instant, with the adaptation frozen.
The simulation is an extreme example, but illustrates that the time-domain algorithm followed the control signal instantaneous frequency. In practice, in the case of RF signal cancellation, the algorithm cannot follow the RF waveform. Thus, the adaptation is done using historical information, but still time-domain data, possibly down converted ve and/or vi signal. This means that there are significant time durations when the control of analog canceller is not optimal. Thus, the signal frequency may be different compared to the location where optimal cancellation is. Furthermore, in some systems there is pre-defined band where the optimal cancellation is wanted. If the vi or ve signal contains signal frequencies on other frequencies as well, those frequency contents may shift the optimal cancellation performance into unwanted band.
Traditionally the control of band where the optimal cancellation is wanted is controlled with very sharp filters. Thus, unwanted frequencies are filtered out before the adaptation process. This way the adaptation may be optimized into the wanted band.